Honeycomb MCP Server

What does “Honeycomb” MCP Server do?

The Honeycomb MCP (Model Context Protocol) Server is a specialized tool designed for Honeycomb Enterprise customers, enabling AI assistants to directly interact with Honeycomb observability data. By acting as a bridge between AI models and the Honeycomb platform, this MCP server allows LLMs to query, analyze, and cross-reference data such as metrics, alerts, dashboards, and even production code behavior. Its integration enhances developer workflows by automating complex data analysis, facilitating quick insights into production issues, and streamlining operations involving SLOs and triggers. The server provides a robust alternative interface to Honeycomb, ensuring that authorized users can leverage AI to gain actionable insights from their observability systems, all while maintaining secure access via API keys and running locally on the user’s machine.

List of Prompts

No prompt templates are explicitly listed in the repository or documentation.

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List of Resources

No explicit list of resources is provided in the available documentation or code overview.

List of Tools

No explicit details about tools (such as functions, endpoints, or tool definitions in server.py or index.mjs) are directly listed in the available documentation or code overview.

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Use Cases of this MCP Server

• Querying Observability Data: Developers can leverage AI to run complex queries across Honeycomb datasets, surfacing trends, anomalies, and key metrics for faster diagnostics.
• SLO and Trigger Insights: AI can pull and interpret service-level objectives (SLOs) and triggers, helping teams stay ahead of performance issues and automate alert analysis.
• Dashboard Analysis: AI can analyze Honeycomb dashboards, summarizing production health, or surfacing significant changes over time.
• Cross-referencing Code and Production Behavior: The server enables AI to link codebase information with real-time production metrics, accelerating root cause analysis and incident response.

How to set it up

Windsurf

1. Prerequisite: Install Node.js 18+ and obtain a Honeycomb API key with full permissions.
2. Build the MCP server:
   • Run pnpm install and pnpm run build.
3. Edit Windsurf configuration file (e.g., windsurf.json).
4. Add Honeycomb MCP Server:

   {
     "mcpServers": {
       "honeycomb": {
         "command": "node",
         "args": [
           "/fully/qualified/path/to/honeycomb-mcp/build/index.mjs"
         ],
         "env": {
           "HONEYCOMB_API_KEY": "your_api_key"
         }
       }
     }
   }
   

5. Restart Windsurf and verify the connection.

Claude

1. Prerequisite: Node.js 18+, Honeycomb API key.
2. Build the server: pnpm install and pnpm run build.
3. Edit Claude configuration file (see CLAUDE.md for more).
4. Add the Honeycomb MCP Server using the following JSON:

   {
     "mcpServers": {
       "honeycomb": {
         "command": "node",
         "args": [
           "/fully/qualified/path/to/honeycomb-mcp/build/index.mjs"
         ],
         "env": {
           "HONEYCOMB_API_KEY": "your_api_key"
         }
       }
     }
   }
   

5. Restart Claude and verify the server is reachable.

Cursor

1. Prerequisite: Node.js 18+, Honeycomb API key.
2. Build with pnpm install and pnpm run build.
3. Edit Cursor’s MCP configuration.
4. Insert the following:

   {
     "mcpServers": {
       "honeycomb": {
         "command": "node",
         "args": [
           "/fully/qualified/path/to/honeycomb-mcp/build/index.mjs"
         ],
         "env": {
           "HONEYCOMB_API_KEY": "your_api_key"
         }
       }
     }
   }
   

5. Restart Cursor and ensure Honeycomb MCP is active.

Cline

1. Prerequisite: Node.js 18+, Honeycomb API key.
2. Build the server: pnpm install and pnpm run build.
3. Edit Cline configuration.
4. Configure as follows:

   {
     "mcpServers": {
       "honeycomb": {
         "command": "node",
         "args": [
           "/fully/qualified/path/to/honeycomb-mcp/build/index.mjs"
         ],
         "env": {
           "HONEYCOMB_API_KEY": "your_api_key"
         }
       }
     }
   }
   

5. Restart Cline and confirm setup.

Note:
Always secure API keys using environment variables. Example:

"env": {
  "HONEYCOMB_API_KEY": "your_api_key"
}

You may also supply multiple environments by repeating the "env" block with different API keys.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "honeycomb": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent can now use this MCP as a tool with access to all its functions and capabilities. Remember to change “honeycomb” to whatever you want to name your MCP server and replace the URL with your own MCP server URL.

Overview

Roots Support: Not mentioned

Between these two tables, the Honeycomb MCP provides a clear integration path and use case description, but lacks public documentation for prompt templates, resources, and tools as per the MCP protocol. It is well-documented for setup and use in enterprise workflows.

Rating: 5/10 — Solid on setup and use-case context, but lacking in technical detail for MCP-specific primitives.

MCP Score